Biology subjects

Orjuela, S.

Publications and source records attributed to Orjuela, S..

4 recordsLinked to original sources

bioRxiv2023

Validation of hypermethylated DNA regions found in colorectal cancers as potential aging-independent biomarkers of precancerous colorectal lesions

BackgroundWe previously identified 16,772 colorectal cancer-associated hypermethylated DNA regions that were also detectable in precancerous colorectal lesions (preCRCs) and unrelated to normal mucosal aging. We have now conducted a study to validate 990 of these differentially methylated DNA regions (DMR) in a new series of preCRCs. MethodsWe used targeted bisulfite sequencing to validate these 990 potential biomarkers in 59 preCRC tissue samples (41 conventional adenomas, 18 sessile serrated lesions), each with a patient-matched normal mucosal sample. Based on differential DNA methylation tests, a panel of (candidate) DMRs was chosen on a subset of the (our) cohort and validated on the remaining part of our cohort and (two) further publicly available datasets with respect to their stratifying potential between preCRCs and normal mucosa. ResultsStrong statistical significance for the difference in methylation levels was observed across the full set of 990 investigated DMRs. From these, a selected candidate panel of 30 DMRs correctly identified 58/59 tumors (area under the receiver operating curve: 0.998). ConclusionsThese validated DNA hypermethylation markers can be exploited to develop more accurate noninvasive colorectal tumor screening assays.

genomics↗

bioRxiv2019

DAMEfinder: A Method to Detect Differential Allele-Specific Methylation

DNA methylation is a highly studied epigenetic signature that is associated with regulation of gene expression, whereby genes with high levels of promoter methylation are generally repressed. Genomic imprinting occurs when one of the parental alleles is methylated, i.e, when there is inherited allele-specific methylation (ASM). A special case of imprinting occurs during X chromosome inactivation in females, where one of the two X chromosomes is silenced, in order to achieve dosage compensation between the sexes. Another more widespread form of ASM is sequence dependent (SD-ASM), where ASM is linked to a nearby heterozygous single nucleotide polymorphism (SNP).\n\nWe developed a method to screen for genomic regions that exhibit loss or gain of ASM in samples from two conditions (treatments, diseases, etc.). The method relies on the availability of bisulfite sequencing data from multiple samples of the two conditions. We leverage other established computational methods to screen for these regions within a new R package called DAMEfinder. It calculates an ASM score for all CpG sites or pairs in the genome of each sample, and then quantifies the change in ASM between conditions. It then clusters nearby CpG sites with consistent change into regions.\n\nIn the absence of SNP information, our method relies only on reads to quantify ASM. This novel ASM score compares favourably to current methods that also screen for ASM. Not only does it easily discern between imprinted and non-imprinted regions, but also females from males based on X chromosome inactivation. We also applied DAMEfinder to a colorectal cancer dataset and observed that colorectal cancer subtypes are distinguishable according to their ASM signature. We also re-discover known cases of loss of imprinting.\n\nWe have designed DAMEfinder to detect regions of differential ASM (DAMEs), which is a more refined definition of differential methylation, and can therefore help in breaking down the complexity of DNA methylation and its influence in development and disease.

bioinformatics↗

bioRxiv2019

ARMOR: an Automated Reproducible MOdular workflow for preprocessing and differential analysis of RNA-seq data

The extensive generation of RNA sequencing (RNA-seq) data in the last decade has resulted in a myriad of specialized software for its analysis. Each software module typically targets a specific step within the analysis pipeline, making it necessary to join several of them to get a single cohesive workflow. Multiple software programs automating this procedure have been proposed, but often lack modularity, transparency or flexibility. We present ARMOR, which performs an end-to-end RNA-seq data analysis, from raw read files, via quality checks, alignment and quantification, to differential expression testing, geneset analysis and browser-based exploration of the data. ARMOR is implemented using the Snakemake workflow management system and leverages conda environments; Bioconductor objects are generated to facilitate downstream analysis, ensuring seamless integration with many R packages. The workflow is easily implemented by cloning the GitHub repository, replacing the supplied input and reference files and editing a configuration file. Although we have selected the tools currently included in ARMOR, the setup is modular and alternative tools can be easily integrated.

bioinformatics↗

bioRxiv2018

The proto CpG island methylator phenotype of sessile serrated adenoma/polyps

Sessile serrated adenomas/polyps (SSA/Ps) are the putative precursors of the {small tilde}20% of colon cancers with the CpG island methylator phenotype (CIMP), but their molecular features are poorly understood. We used high-throughput analysis of DNA methylation and gene expression to investigate the epigenetic phenotype of SSA/Ps. Fresh-tissue samples of 17 SSA/Ps and (for comparison purposes) 15 conventional adenomas (cADNs)--each with a matched sample of normal mucosa-- were prospectively collected during colonoscopy (total no. samples analyzed: 64). DNA and RNA were extracted from each sample. DNA was subjected to bisulfite next-generation sequencing to assess methylation levels at {small tilde}2.7 million CpG sites located predominantly in gene regulatory regions and spanning 80.5Mb ({small tilde}2.5% of the genome); RNA was sequenced to define the samples transcriptomes. An independent series of 61 archival lesions was used for targeted verification of DNA methylation findings. Compared with normal mucosa samples, SSA/Ps and cADNs exhibited markedly remodeled methylomes. In cADNs, hypomethylated regions were far more numerous (18,417 vs 4288 in SSA/Ps) and rarely affected CpG islands/shores. SSA/Ps seemed to have escaped this wave of demethylation. Cytosine hypermethylation in SSA/Ps was more pervasive (hypermethylated regions: 22,147 vs 15,965 in cADNs; hypermethylated genes: 4938 vs 3443 in cADNs) and more extensive (region for region), and it occurred mainly within CpG islands and shores. Given its resemblance to the CIMP typical of SSA/Ps putative descendant colon cancers, we refer to the SSA/P methylation phenotype as proto-CIMP. Verification studies of six hypermethylated regions (3 SSA/P-specific and 3 common) demonstrated the high potential of DNA methylation markers for predicting the diagnosis of SSA/Ps and cADNs. Surprisingly, proto-CIMP in SSA/Ps was associated with upregulated gene expression (n=618 genes vs 349 that were downregulated); downregulation was more common in cADNs (n=712 vs 516 upregulated genes). The epigenetic landscape of SSA/Ps differs markedly from that of cADNs. These differences are a potentially rich source of novel tissue-based and noninvasive biomarkers that can add precision to the clinical management of the two most frequent colon-cancer precursors.

cancer biology↗